Yield Stability of Newly Released Wheat Varieties in Multi-environments of Bangladesh

_____________________________________________________________________________________________________ *Corresponding author: E-mail: [email protected];

International Journal of Plant & Soil Science 6(3): 150-161, 2015; Article no.IJPSS.2015.106

ISSN: 2320-7035

SCIENCEDOMAIN international www.sciencedomain.org

Yield Stability of Newly Released Wheat Varieties in Multi-environments of Bangladesh

Md. Ashraful Alam1*, Zahidul Islam Sarker1, Md. Farhad1, Md. Abdul Hakim1, Naresh Chandra Dev Barma2, Md. Israil Hossain3, Md. Mokhlesur Rahman4

and Rabiul Islam5

1Wheat Research Centre (WRC), Bangladesh Agricultural Research Institute (BARI), Dinajpur, Bangladesh.

2Regional Wheat Research Centre (RWRC), Bangladesh Agricultural Research Institute (BARI),

Joydebpur, Gazipur, Bangladesh. 3Regional Wheat Research Centre (RWRC), Bangladesh Agricultural Research Institute (BARI),

Rajshahi, Bangladesh.

4Regional Agricultural Research Station (RARS), Bangladesh Agricultural Research Institute (BARI),

Jamalpur, Bangladesh. 5Regional Agricultural Research Station (RARS), Bangladesh Agricultural Research Institute (BARI),

Jessore, Bangladesh.

Authors’ contributions

This study was carried out in collaboration among the authors. All authors managed the literature searches, read and approved the final manuscript.

Article Information

DOI: 10.9734/IJPSS/2015/14824

Editor(s): (1) Sławomir Borek, Department of Plant Physiology, Adam Mickiewicz University, Poland.

(2) Marco Aurelio Cristancho, National Center for Coffee Research, Chinchiná, Caldas, Colombia. (3) A. Mujib, Department of Botany, Hamdard University, India.

Reviewers: (1) Anonymous, Bangladesh.

(2) Azhar A. Kham, Bahauddin Zakariya University, College of Agriculture ,Layyah, Pakistan. (3) Anonymous, China.

Complete Peer review History: http://www.sciencedomain.org/review-history.php?iid=955&id=24&aid=8312

Received 23rd

October 2014 Accepted 17th February 2015

Published 28th

February 2015

ABSTRACT

Varietal adaptability to environmental fluctuations is important for the stabilization of crop production on both region and season. In this context, one thousand six trials with four wheat varieties viz., BARI Gom 25, BARI Gom 26, BARI Gom 27 and BARI Gom 28 were conducted in forty districts of 8 agricultural regions in Bangladesh during 2013-14. Objectives of this study were to evaluate the mean yield performance and yield stability of the newly released varieties in

Original Research Article

Alam et al.; IJPSS, 6(3): 150-161, 2015; Article no.IJPSS.2015.106

151

different regions of the country in different environmental status. Yield of 4 varieties significantly varied among the locations. The highest mean yield was obtained from Rangpur region (4275.15 kg ha-1) followed by Jessore region (4266 kg ha-1). Yield of these two regions were statistically similar but significantly higher than other regions. In AMMI analysis, AMMI-1 biplot showed that Rangpur and Rajshahi were favorable environments; Sylhet and Comilla were unfavorable environments. Considering varieties, BARI Gom 26 and BARI Gom 25 were more stable and can be recommended for all environmental condition of Bangladesh. On the other hand, BARI Gom 27 was the most sensitive to changing environments. In AMMI-2 biplot, Comilla, Sylhet and Dhaka were the most discriminating environments for all tested varieties, while BARI Gom 26 and BARI Gom 25 were more stable varieties. The most suitable varieties for Rangpur, Rajshahi and Mymensingh was BARI Gom 26; BARI Gom 25 for Sylhet; BARI Gom 27 for Comilla, Jessore and Barishal while BARI Gom 28 was suitable for Dhaka. Among the varieties, BARI Gom 26 and BARI Gom 25 had higher mean grain yield values coupled with small positive IPCA 1 scores and hence these are recommended for all the locations.

Keywords: Yield stability; Triticum aestivum; adaptability; AMMI analysis.

ABBREVIATION PTOS- Power tiller operated seeder; BpLB-Bipolaries Leaf Blight, BARI- Bangladesh agricultural research institute; WRC- Wheat research centre; MET- Multi-environment trials; AMMI- Additive main effect and multiplication interaction; GGE- Genotype main effects and genotype × environment interaction effects; PCA- Principal component analysis; DAE- Department of agricultural extension; NGOs- Non-government organizations; OFRD- On-farm research division; CSISA- Cereal systems initiative for south Asia; ARS- Agricultural research station; SAAOs- Sub-assistant agricultural officers; SA- Scientific assistance; SSAs- Senior scientific assistances; UAOs- Upazila agriculture officers; CPS- Crop protection specialist; IPCA- Interaction principal component axes; GEI- Genotyp-by-environment interaction; ASV- AMMI stability value.

1. INTRODUCTION The agricultural sector plays a very significant role in the Bangladesh economy as well socially and culturally. Agriculture accounts for about 20% of the country’s Gross Domestic Product (GDP) [1]. In addition, agriculture provides more than 60% of the national employment. The agriculture sector is an important tool to reach different goals stated in the Poverty Reduction Strategy as it directly reaches the poorest in the rural areas, providing both food and cash at the same time. Wheat is the second most important cereal crop after rice in Bangladesh [2] and its consumption is increasing 3% per year [3]. The tragic irony is that the wheat production in the country 1.4 million tons [4], which is much bellow than the annual requirement. This deficit accounts about 75% of country’s annual consumption and it is met, through imports [5]. Wheat production has increased steadily from around 0.115 million tons in 1971-72 to 1.9 million tons in 1999 and then gradually decreased to 0.763 million tons in 2006-07 [6,7]. This low production is mainly due to the reduction of area under wheat cultivation. The

highest wheat growing area was 0.85 million hectares in 1999 which now came down to 0.45 (2013-14) million ha [8]. Wheat is to compete with other profitable crops like Boro rice, corn, potato and winter vegetables which insisting farmers to push wheat crop in marginal lands from fertile ones and this is considered as the main reason for decreasing area under wheat [9]. Yield was also decreasing due to cultivation of old varieties that are susceptible to leaf rust and BpLB, knowledge gap about recommended technologies [10]. For increasing wheat production in Bangladesh, It is very important to adopt new technologies recommended by Wheat Research Centre for increasing wheat production in Bangladesh [11]. Use of power tiller operated seeder (PTOS) to confirm timely seeding and use of wheat thresher is also important to save the quality of seeds from early monsoon. WRC of Bangladesh Agricultural Research Institute (BARI) has released 30 varieties in conventional breeding approach and most of the later released varieties are better than previous one [12] in respect of yield, disease and terminal heat tolerance. But those varieties and other technologies are not


152

being adopted by the farmers in a satisfactory rate due to their inadequate knowledge about the varieties and technologies; and insufficient extension efforts [13]. Crop adaptability is commonly understood as wide or local adaptation as a result of high mean grain yield and yield stability across environments and seasons. On the other hand, yield stability of a cultivar is defined as consistency of yield response of variety(ies) across environments or seasons [14]. Varietal adaptability to environmental fluctuations is important for the stabilization of crop production on both region and season. Information on the genotype x environment interaction is essential to the successful evaluation of stable genotypes [15]. Multi-environment trials (MET) are conducted to evaluate the yield stability performance of different genetic materials under different environmental conditions [15,16]. Oftentimes, genotypes subject to different environments show significant fluctuations on the yield responses. These changes are influenced by different environmental conditions commonly referred to as genotype-by-environment (GE) interaction [17]. The action of GE interaction makes genetic progress difficult since it minimizes the association of phenotypic and genotypic values [18]. Hence, GE interaction should be exploited through selection of superior genotypes on specific location or avoided by selecting genotypes with wide adaptability and stable yield response across diverse locations [19]. Recently, several methods in analyzing GE interaction and MET data to reveal patterns of GE have been reported. These methods include regression coefficient [20], sum of squared deviations from regressions [21], stability of variance [22], coefficient of determination [23] coefficient of variability [24], and multiplicative main effects and multiplicative interaction (AMMI) [25,26,27] which have been commonly used. Another method proposed by Yan et al. [15] called GGE biplot analysis for graphical display of GE interaction pattern in MET data. GGE biplot is an effective method based on principal component analysis (PCA) which graphically displays GE in a two-way table and allows visual examination of the relationship interactions among test environments, genotypes, and GE interaction. The main objective of this study was to evaluate the yield performance and yield stability of the newly released varieties in different regions of the country in different environmental status.

2. MATERIALS AND METHODS

One thousand six trials were conducted with 4 newly released wheat varieties viz., BARI Gom 25 & BARI Gom 26 (released 2010) and BARI Gom 27 & BARI Gom 28 (released 2012) in the farmers’ fields of forty districts out of sixty four under 8 agricultural regions in 2013-14 (Table 1 & Fig. 1) in collaboration with Department of Agricultural Extension (DAE), Non-government Organizations (NGOs), Cereal Systems Initiative for South Asia (CSISA), On Farm Research Division (OFRD), & Agricultural Research Station (ARS) of BARI. Seven hundred twenty three trials were conducted in collaboration with DAE, eighty through NGOs, forty through CSISA and one hundred fifty one trials by BARI scientists (thirty six through WRC & ARS and one hundred fifteen through OFRD). The pedigree of these varieties are BARI Gom 25 (ZSH 12/HLB 19//2*NL 297), BARI Gom 26 (ICTAL123/3/RAWAL87//VEE/HD2285), BARI Gom 27 (FRANCOLIN # 1) and BARI Gom 28 (CHIL/2*STAR/4/BOW/CROW//BUC/PVN/3/2*VEE#10). Each trial had a single variety with a plot size of 800 m

2. with no replication. Each of the

variety was allotted to a single farmer. Only seed, fertilizers, signboard and printed documents on wheat production technologies were supplied to farmers. All other management practices were done by farmers. A day long training was given to the farmers, related Sub Assistant Agricultural Officer (SAAOs), Scientific Assistance (SA)/Senior Scientific Assistances (SSAs), Upazila Agriculture Officers (UAOs) and Crop Protection Specialist (CPS) of the respective districts about trials, wheat seed production and preservation technologies. Seeds were sown from 10 November to 16 December 2013. Weekly temperature data recorded during the growing period of wheat is graphically presented in Figs. 2a & 2b.

Yields were collected from 20 m2 (4 samples×5 m2) areas of each trial. A data collection template was supplied to farmers for sending data on yield, sowing & harvesting date, number of irrigations, seed preservation, overall comments, etc. through supervising officials of DAE, NGOs and BARI. In AMMI model, the contribution of each genotype and each environment to the GEI is assessed by the use of the biplot graph display where yield means are plotted against the scores of the IPCA1 [27]. The stability parameters and regression coefficient (bi) were estimated


153

according to Eberhart and Russell [21]. All the data were subjected to analysis using two software prgrammes: Cropstat version 7.2 after Zobel et al. [27] and SAS. 9.

The AMMI model does not make provision for a quantitative stability measure, such a measure is essential in order to quantify and rank genotypes

according to their yield stability, the following measure was proposed by Purchase [28]:

�� = ��

��1��

��2 �� (��1 ��)�

�

+[��2 ��]²

Fig. 1. Showing trials located in different regions of Bangladesh


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Fig. 2a. Weather data containing minimum, maximum and average temperature of Rangpur, Rajshahi, Jessore and Mymensing region during the experimental season


155

Fig. 2b. Weather data containing minimum, maximum and average temperature of Dhaka, Comilla, Sylhet and Barisal region during the experimental season


156

3. RESULTS AND DISCUSSION

3.1 Agronomic Performance

Yield of 4 varieties significantly (F(8,20) =8.65; P= <0.001) varied among the locations. The highest mean yield was obtained from Rangpur region (4275.15 kg ha

-1) followed by Jessore

region (4266 kg ha-1). Yield of these two regions were statistically similar but significantly higher than other regions (Table 2). The lowest mean yield was recorded in Sylhet region due to the water stress. Hasan et al. [29] found that area and production of wheat were fluctuating due to weather condition and technological changes based on secondary data during 1980/81-2003/04. This reference support current findings of variation in wheat yield among locations. The yield of BARI Gom 26 was highest (4725 kg ha-1) in Rangpur region followed by BARI Gom 28 (4530 kg ha-1) in Dhaka region and BARI Gom 25 (4370.70 kg ha-1) in Jessore region. At Rajshahi region, the yield of BARI Gom 26 was highest (4302.29 kg ha-1) followed by BARI Gom 25 (4006.71 kg ha

-1). The BARI Gom 27

produced lowest yield (27.20 kg ha-1) in Sylhet region. The highest mean yield was recorded in BARI Gom 26 (3977.83 kg ha

-1) followed by

BARI Gom 28 (3828.01 kg ha-1), BARI Gom 25 (3819.31 kg ha

-1) and BARI Gom 27 (3709.78 kg

ha-1). BARI Gom 26 produced the highest yield but statistically (F(3,20) =1.42; P= 0.265) similar with all other varieties. Mohammed [30] observed in Northern Sudan using AMMI analysis that considering the average performance combined over environments, the genotypes differed significantly in grain yield of bread wheat. Naroui Rad et al. [31] also found variation in grain yield of bread wheat lines due to environmental variation.

The farmer have preserved total 95426 kg seeds of the new varieties. The highest seed stored (27925 kg) in Jessore region followed by

Rajshahi region (23423 kg). In total 50050 farmers of the same and neighboring villages visited the trials and expressed their interest to collect seeds of new varieties for the next year. These results demonstrated that all of 4 new varieties (BARI Gom 25, BARI Gom 26, BARI Gom 27 and BARI Gom 28) were preferred by the farmers.

3.2 Stability Performance Adaptability of the new wheat varieties over wide ranges of environments (40 districts) are presented in Fig. 3 which indicates that BARI Gom 25 and BARI Gom 26 were more stable than other varieties. These varieties had comparatively higher yields in poor environment and can be recommended for growing in poor environments. BARI Gom 27 was more sensitive to changing production environments. It had low yield in poor and very high yields in better environments. This variety could be recommended for growing in favourable environments. BARI Gom 28 had average sensitivity to changing production environments.

3.3 AMMI Biplot Analysis Table 3 reflects IPCA score and AMMI stability value (ASV). BARI Gom 28 showed the lowest scores in the IPCA 1, followed by BARI Gom 26 and BARI Gom 25. BARI Gom 27 scored the highest IPCA 1 value. Considering The AMMI stability value (ASV) that takes into account the scores of the IPCA 2, the variety BARI Gom 28 appeared to be among those showing low ASV. The variety BARI Gom 26 became the second lowest in ASV. With regards to locations, Rangpur and Jessore gave the lowest IPCA 1 scores whereas Mymensingh, Rajshai and Dhaka gave medium score and Comilla scored the highest IPCA 1 value.

Table 1. Regional and Institutional distribution of wheat trials in the year 2013-14

Sl # Region Number of trials DAE NGO WRC & ARS CSISA OFRD Total

1 Rangpur 204 36 12 0 0 252 2 Rajshahi 190 36 12 0 0 238 3 Jessore 116 0 0 0 24 140 4 Mymensingh 72 0 12 36 12 132 5 Dhaka 24 0 0 0 15 39 6 Comilla 12 0 0 0 12 24 7 Sylhet 12 0 0 0 12 24 8 Barisal 93 12 0 12 40 157 Total 723 84 36 48 115 1006


157

Table 2. Region-wise results of variety trials conducted through all organization (DAE, OFRD, ARS, WRC, CSISA & NGOs) in 2013-14

Districts No. of trials

Result of trials

Grain yield (kg/ha) Seeds preserved by farmers (kg)

BARI Gom25

BARI Gom26

BARI Gom27

BARI Gom28

Mean yield ± SE

Rangpur 252 98 4002.50 4725.00 4116.50 4256.60 4275.15±4.45 a 17281 Rajshahi 238 118 4006.71 4302.29 3739.40 3895.71 3986.03±3.85 abc 23423 Jessore 140 140 4370.70 4212.80 4157.00 4324.40 4266.23±2.48 a 27925 Mymensingh 132 132 3692.88 3755.25 3363.63 3703.63 3628.84±3.34 bdc 11780 Dhaka 39 39 3983.33 4180.00 4050.00 4530.00 4185.83±3.90 ab 4770 Comilla 24 19 3290.00 3445.00 3840.00 3325.00 3475.00±3.97 cd 1807 Sylhet 24 10 3715.00 3640.00 2720.00 2950.00 3256.25±5.57 d 1170 Barishal 157 93 3493.33 3562.33 3691.67 3638.75 3596.52±2.33 bdc 7270 Total 1006 649 95426 Mean 3819.31 3977.83 3709.78 3828.01

Table 3. IPCA axes scores for genotypes and environments, AMMI stability value (AVS) and mean performance for grain yield (kg/ha) of 4 wheat

varieties grown at 8 agricultural regions in Bangladesh (2013-14) Variety Yield Rank IPCA1

Score IPCA2 Score

bi ASV Rank Location IPCA1 Score

BARI Gom 25 3819.31 3 16.71 8.955 0.697 57.7 3 Rangpur 0.52 BARI Gom 26 3977.83 1 13.01 -4.08 0.994 44.6 2 Rajshahi 6.39 BARI Gom 27 3709.78 4 -20.75 11.89 1.007 71.8 4 Jessore -1.52 BARI Gom 28 3828.01 2 -8.959 -16.76 1.303 34.9 1 Mymensingh 3.43 Dhaka -8.32 Comilla -14.56 Sylhet 23.22 Barishal -9.15

The AMMI1 biplot provide a graphical representation of the relationship between the first interaction principal component axis (AMMI component 1) and mean of varieties and location [32] with the biplot according for up to 91.5% the treatment sum of squares (Fig. 4). Variety and locations on the same parallel line, relative to ordinate, have similar yields and a variety or location on the right side of the midpoint of this axis has higher yields than those on the left hand side [27]. According to AMMI-1 biplot analysis (Fig. 4), environments showed high variation in both main effects and interactions (IPCA1). While the Rangpur and Jessore had low interaction; Sylhet and Comilla were highly interactive. Rajshahi was the most favorable environment and Comilla and Barishal was the least favorable environments, while Mymensingh and Dhaka were the average environments. Environment Rangpur and Jessore with IPCA1 scores near zero had no interaction effects, Variety BARI Gom 26 and location Rangpur combinations with IPCA1 scores of the same sign produced positive specific interaction effects and BARI Gom 28 and location Jessore combinations with IPCA1 score produced positive specific interaction effectsHagos and Abay [33] also found tvariation among genotype, location and genotype by location interaction in bread wheat by AMMI biplot analysis.

Consequently, the variety BARI Gom 26 was the high yielder followed by BARI Gom 28, BARI Gom 25 and BARI Gom 27. The AMMI1 estimation had a profound effect in producing clear and stratified ranking patterns and on this basis BARI Gom 26 and BARI Gom 25 would be

Fig. 3. Adaptability of the new wheat varieties conducted by DAE

Alam et al.; IJPSS, 6(3): 150-161, 2015; Article no.IJPSS.201

158

The AMMI1 biplot provide a graphical representation of the relationship between the first interaction principal component axis (AMMI component 1) and mean of varieties and location [32] with the biplot according for up to 91.5% of the treatment sum of squares (Fig. 4). Variety and locations on the same parallel line, relative to ordinate, have similar yields and a variety or location on the right side of the midpoint of this axis has higher yields than those on the left hand

1 biplot analysis (Fig. 4), environments showed high variation in both main effects and interactions (IPCA1). While the Rangpur and Jessore had low interaction; Sylhet and Comilla were highly interactive.

rable environment and Comilla and Barishal was the least favorable environments, while Mymensingh and Dhaka were the average environments. Environment Rangpur and Jessore with IPCA1 scores near zero had no interaction effects, Variety BARI

ion Rangpur combinations with produced positive

specific interaction effects and BARI Gom 28 and location Jessore combinations with IPCA1 score produced positive specific interaction effects.

[33] also found the significant genotype, location and genotype

by location interaction in bread wheat by AMMI

Consequently, the variety BARI Gom 26 was the high yielder followed by BARI Gom 28, BARI Gom 25 and BARI Gom 27. The AMMI1

mation had a profound effect in producing clear and stratified ranking patterns and on this basis BARI Gom 26 and BARI Gom 25 would be

considered more adapted to a wide range of environments than the rest of the varieties. BARI Gom 27 had low yield and unstable and BARI Gom 28 had high yield but moderately stable. Dixon and Nukenine [34] and Crossa obtained a similar stratification in cassava and wheat genotypes, respectively.

According to AMMI-2 biplot analysis (Fig. 5), the varieties (and environments) that are located far away from the origin are more responsive. BARI Gom 27 BARI Gom 28 appeared unstable due to their dispersed position. Comilla, Sylhet and Dhaka were the most differentiating environments, while BARI Gom 27 and BGom 28 were the most responsive genotypes. BARI Gom 26 and BARI Gom 25 appeared to be more stable when plotting the IPCA1 and IPCA2 scores. Genotypes and environments that fall in the same sectors interact positively; in contrast if they fall in opposite sectors interact negatively. If they fall into adjacent sectors, interaction is somewhat more complex. In this case, the best variety with respect to sites Dhaka was BARI Gom 28 but unsuitable for Sylhet. Gom 25 was suitable for Sylhet but unsuitable for Dhaka. With respect to Comilla, Barishal and Dhaka, variety BARI Gom 27 was particularly suitable but unsuitable for Rajshahi and Mymensingh, while variety BARI Gom 26 was well fit for Rajshahi and Mymensimultivariate approach, the AMMI model is better for partitioning the G x E into the causes of variation, which ease identification of environments’ potential and is used to identify superior genotypes either with specific adaptation or wide adaptation [32,36,

Fig. 3. Adaptability of the new wheat varieties conducted by DAE

, 2015; Article no.IJPSS.2015.106

considered more adapted to a wide range of environments than the rest of the varieties. BARI

and unstable and BARI high yield but moderately stable.

Dixon and Nukenine [34] and Crossa et al. [35] obtained a similar stratification in cassava and

2 biplot analysis (Fig. 5), the eties (and environments) that are located far

away from the origin are more responsive. BARI Gom 27 BARI Gom 28 appeared unstable due to their dispersed position. Comilla, Sylhet and Dhaka were the most differentiating environments, while BARI Gom 27 and BARI Gom 28 were the most responsive genotypes. BARI Gom 26 and BARI Gom 25 appeared to be more stable when plotting the IPCA1 and IPCA2 scores. Genotypes and environments that fall in the same sectors interact positively; in contrast if

te sectors interact negatively. If they fall into adjacent sectors, interaction is somewhat more complex. In this case, the best

with respect to sites Dhaka was BARI Gom 28 but unsuitable for Sylhet. Variety BARI

le for Sylhet but unsuitable for Dhaka. With respect to Comilla, Barishal and

BARI Gom 27 was particularly suitable but unsuitable for Rajshahi and

BARI Gom 26 was well fit for Rajshahi and Mymensingh. For multivariate approach, the AMMI model is better for partitioning the G x E into the causes of variation, which ease identification of environments’ potential and is used to identify superior genotypes either with specific

36,37].


159

Fig. 4. AMMI 1 model for grain yield (kg ha-1) showing the means of varieties and locations against their respective IPCA 1 scores. RN = Rangpur, RJ= Rajshahi, JE= Jessore,

MY= Mymensingh, DH= Dhaka, CO= Comilla, SY= Sylhet and BA= Barishal, 1 = BARI Gom 25, 2 = BARI Gom 26, 3 = BARI Gom 27, 4 = BARI Gom 28, Δ = Variety combination (genotypes),

● = Locations (8)

Fig. 5. AMMI 2 model for grain yield (kg ha-1) showing the IPCA scores of wheat varieties planted across locations


160

4. CONCLUSION The AMMI statistical model has been used to diagnose the GE interaction pattern of grain yield of newly released wheat varieties. The most suitable varieties for Rangpur, Rajshahi and Mymensingh were BARI Gom 26; BARI Gom 25 for Sylhet; BARI Gom 27 for Comilla, Jessore and Barishal while variety BARI Gom 28 was suitable for Dhaka. BARI Gom 26 and BARI Gom 25 were hardly affected by the GE interaction and thus will perform well across a wide range of environments and these can be recommended for all locations. Locations, such as Rangpur and Rajshahi, could be regarded as a good selection sites for wheat cultivation for it’s stable yielding environment.

ACKNOWLEDGEMENTS The authors are highly thankfull to the farmers who were actively involved in trial management in their own lands. The author also acknowledge to the DAE and NGO representatives who have legitimate contribution to the trial management in the remote locaton. This experiment was carried out by Government fund of BARI.

COMPETING INTERESTS Authors have declared that no competing interests exist.

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Yield Stability of Newly Released Wheat Varieties in Multi-environments of Bangladesh

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